Maybe Higher Interest Rates or High CAPE Ratios Will Not Derail AI Investment

The issue economists and financial analysts always face when attempting to assess the impact of higher interest rates on equity valuations in general or artificial intelligence in specific is that evaluations must be made on the assumption that “all other things remain equal,” which, of course, is rarely the case. Markets are dynamic, so, by definition, all else does not remain equal. 

Consider the expected impact of price-earnings ratios and higher real interest rates. 

The cyclically adjusted price-earnings ratio (CAPE) being at a historically-high level suggests lower returns from equity markets over the next decade or so and a possible "reversion to mean" for AI equity values.

But the high CAPE ratio does not mean artificial intelligence public equities are necessarily overvalued or in a “bubble.” It does mean that AI firms must grow into their valuations by continued high earnings growth. 

Observation	What CAPE suggests
U.S. equities are expensive relative to historical earnings	Strongly supported
Long-term expected real returns are probably lower than historical averages	Historically supported
AI stocks specifically are overvalued	Not established by CAPE
AI is a speculative bubble	CAPE cannot establish this
AI earnings expectations are unusually important to market valuation	Yes
A substantial earnings disappointment could have disproportionate market consequences	Yes
Higher interest rates would be especially consequential	Yes
AI productivity could eventually justify some of today's valuation	Possible
Today's valuation requires unusually strong future earnings growth	Yes

Is today's high CAPE a denominator problem or a numerator problem? The denominator problem happens if investors have simply bid up stocks too far relative to sustainable earnings.

The numerator problem happens if today's earnings substantially understate the future earnings power of the companies because AI is about to raise productivity and profits dramatically. Take your pick., 

The second possibility is the "this time is different" argument, an obvious red flag for most of us. Historically, that argument has sometimes been correct economically while still being wrong financially.

In the shorter term, investors will have to evaluate the likely impact of higher interest rates (the “real” rate being the thing that matters) on the AI ecosystem. 

The conventional wisdom is that higher real interest rates (nominal rate minus inflation) tend to slow economic growth. That should mean the AI ecosystem also grows more slowly, as higher financing costs reduce investment and therefore potentially future revenue growth.

Also, the present value of distant future cash flows falls when the discount rate rises. This can be particularly severe for AI companies because much of their expected value is based on future rather than current earnings.

Part of AI ecosystem	Likely effect of higher rates	Why
Hyperscaler AI capex	Moderate slowdown	Higher cost of capital raises the hurdle rate for marginal data centers, GPUs and power projects. But Microsoft, Alphabet, Amazon and Meta have enormous cash flows and strategic reasons to keep investing.
Frontier-model companies	Moderate-to-large pressure	Companies with large compute bills and limited current profits become more dependent on external capital. Higher rates make investors demand a clearer path to monetization.
AI startups	Large pressure	Valuations depend heavily on discounted future cash flows and access to venture capital. Higher rates particularly hurt companies whose revenues are distant or speculative.
Data-center developers	Moderate-to-large pressure	These are extraordinarily capital-intensive, long-duration projects. Financing costs directly affect project economics.
GPU/accelerator suppliers	Initially modest; eventually meaningful	Existing compute shortages and contractual commitments can insulate near-term demand. But slower infrastructure deployment eventually feeds back into equipment orders.
Power/electrical infrastructure	Moderate pressure	Projects with long lead times and large upfront investment become harder to finance, although genuine power scarcity can preserve demand for some projects.
Cloud AI services	Mixed	Higher rates can restrain customers' IT budgets, but AI can also be justified as a way to reduce labor costs or increase productivity.
Enterprise AI software	Mixed/slightly negative initially	Discretionary experiments are vulnerable, while applications with measurable ROI may actually become more attractive if companies are under pressure to improve productivity.
AI applications with little capex	Relatively resilient	Higher rates don't materially change their marginal cost of development or deployment.
AI infrastructure with long-term contracts	More resilient	Contracted cash flows can support project financing even when the general cost of capital rises.
Existing profitable technology companies	Relatively resilient	High cash generation makes them less dependent on external financing.

Also, it matters greatly “why” interest rates are climbing. If rates rise because the economy is strong and inflation remains persistent, that is a very different scenario from rates rising because of a recession. And, at the moment, U.S. gross domestic product is still growing, according to the Federal Reserve.

Higher rates “should” put pressure on some suppliers in the AI value chain, such as high-performance-compute-as-a-service suppliers. But some will argue that better capital investment discipline will result. 

Higher rates might even help suppliers of used graphics processor units, as such units might retain their value better.

Effect of higher rates	Likely impact on AI
Higher cost of debt	Negative for leveraged AI infrastructure
Higher required return on new projects	Negative for marginal data centers and GPU deployments
Lower equity valuations	Negative, particularly for startups dependent on new funding
More expensive private credit	Negative for neoclouds and infrastructure developers
Pressure on hyperscaler free cash flow	Negative, potentially causing capex discipline
Higher discount rate applied to future AI profits	Negative for valuations
Stronger incentive to monetize existing GPUs	Mixed/positive—could increase utilization
Higher hurdle rate for speculative projects	Potentially positive for industry discipline
Cash-rich hyperscalers' ability to self-finance	Mitigates the effect

As usual, higher borrowing costs will be a negative for startups and smaller firms more reliant on borrowed money. 

AI layer	Rate sensitivity
Nvidia/AMD-type highly profitable chip suppliers	Low–moderate
Hyperscalers	Moderate
Large profitable AI software companies	Moderate
AI infrastructure developers	High
Neoclouds	Very high
Frontier-model startups	High
Early-stage AI startups	Very high

Still, ceteris paribus (“all other things being equal”) rarely describes events in the real world. All other things will not remain equal. Higher interest rates “should” slow AI investment. But competitive pressures within the industry, evidence of value and financial impact, capital availability and all sorts of other potential macroeconomic influences are likely to have an effect as well.

So interest rate increases might not have the slowing impact one might otherwise expect. Nor might the impact of a high CAPE necessarily cause the bursting of the "AI bubble." There are simply too many moving parts.

How Much Will AI Affect Critical Thinking?

At the risk of seeming flippant, artificial intelligence probably will not negatively affect “critical thinking” very much. 

The reason has less to do with “AI giving one the answers” and more the reality that critical thinking skills likely will remain a bell curve. Though controversial for social reasons, bell curves are widely found in nature.  

source 

Also, such skills are domain specific, researchers suggest. People reason using their knowledge of a particular subject matter, and skills learned in one domain do not automatically transfer to another, researchers suggest. 

not a capability that “most students” automatically acquire to a high level simply by going through school or college. Programs in school meant to teach critical thinking skills have had limited success, some suggest. 

Richard Arum and Josipa Roksa's longitudinal Collegiate Learning Assessment project, which followed more than 2,300 students at 24 four-year institutions, found that 45 percent of students showed no statistically significant improvement in critical thinking, complex reasoning and written communication during their first two years of college.

And the pattern of skill improvements might well suggest that preexisting conditions (skills, aptitude, experience) shape the extent of skills acquisition. 

Another study found some changes after four years of college work, though generally of “small” dimensions. The average amount of improvement is modest, and the distribution of outcomes is wide, which is about what a bell curve distribution might suggest would be the case. 

In other words, the causality might be that low critical thinking skills lead to high AI dependence, rather than high AI dependence leading to lower critical thinking.

Student type	Pre-AI ability	Likely AI-era behavior
Low reasoning	Low	Accepts plausible answers with little checking
Lower-middle	Moderate	Uses AI effectively for routine tasks but misses subtle errors
Middle	Moderate	Can check obvious errors, struggles with sophisticated ones
Upper-middle	High	Uses AI iteratively and verifies important claims
High	Very high	Challenges AI, generates counterarguments, identifies subtle errors
Expert	Very high + domain knowledge	Uses AI as an intellectual multiplier

A 2024 study suggests some over-reliance and reduced independent decision-making, critical thinking and analytical reasoning among students using AI. Lower cognitive proficiency was found in another study.  

But there probably is not a single bell curve of "critical thinking." There could be separate distributions for different capabilities such as reasoning, knowledge, metacognition, skepticism, evidence evaluation and domain expertise.

Critical thinking might involve some combination of:

• identifying assumptions

• evaluating evidence

• distinguishing correlation from causation

• recognizing alternative explanations

• detecting logical errors

• comparing competing interpretations

• asking appropriate questions

• judging the reliability of information

• drawing justified conclusions

• revising one's beliefs when evidence changes.

But most of us do not use such skills, most of the time, for most daily interactions. In fact, such reasoning might be fairly rare, in real life, for most people, most of the time. 

The unsettled issue is whether, and to what extent, AI will change the shape of the cognitive skills curve. In principle, AI could flatten the curve, if it raises all learner capabilities. But it might also accentuate differences if AI is differentially useful. In other words, the curve might be reshaped.

But such a distribution might also be unusual in nature. If preexisting skills also tend to predict post-AI skill levels, then a standard bell distribution might be the result. 

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Agentic AI Will Reshape Web Ad Economics

“For at least the last at least 30 years, the business model of the internet has been advertising,” says Matthew Prince, Cloudflare CEO. “It’s not the entire business model of the internet, but it’s really driven all of the growth of the web.”

So what happens now that artificial intelligence traffic for training, inference and agentic operations begins to dominate web traffic?

Already, automated traffic has now passed human traffic. “Five years from now, we think that automated traffic will be 1,000 times human traffic,” he says. 

“The challenge of that is, if you have 1,000 times more traffic, someone’s got to pay for the infrastructure to power that,” says Prince. “That’s going to require bandwidth, that’s going to require servers, that’s going to require a lot of things.”

The traditional model of how to pay for that, which was advertising, doesn’t work for bots because they do not click on ads, which destroys the monetization mechanism. 

So the issue is how content providers will create new revenue mechanisms for bot traffic, since ads do not work. 

For that matter, it is not clear how subscription or commerce revenues will be affected, either. 

Traditional web	AI/agentic web
Human is the "customer"	Human may never visit
Page view creates advertising opportunity	Bot request may create no ad impression
Search crawler is economically valuable because it sends traffic	AI crawler can consume content without sending traffic
More traffic generally = more revenue	More bot traffic can = more bandwidth/compute/security cost
SEO means getting a high search ranking	AEO means getting selected/cited by an AI
Affiliate click produces revenue	AI agent may bypass the affiliate link
E-commerce wants customer on its site	Agent may choose product and potentially transact elsewhere
Content is given away in exchange for distribution	Content increasingly becomes a licensable input

All that suggests we might have to invent new ways of generating revenue beyond advertising, almost all of which might involve some form of payment for content. 

Model	How it works	Economic logic
Annual licensing	AI company pays publisher fixed fee	Similar to syndication
Pay-per-crawl	Payment for each page/request	Metered consumption
Pay-per-answer	Payment when content contributes to an answer	Closer to value created
Revenue share	Publisher gets share of AI subscription/ad revenue	Aligns incentives
Referral/affiliate	AI sends user to publisher	Preserves old model
Transaction fee	Website earns money when agent completes transaction	Potentially much larger
API access	AI accesses structured proprietary data	Turns website into data provider

It remains to be seen whether licensing regimes can replace lost advertising revenues, though. Commerce revenues should help, but it is not unreasonable to suggest the new business models might not be as lucrative as the older ad-based models. 

As we have seen in other businesses disrupted by the internet, such as music, subscriptions and events might become more important. In most other cases, it is easier to see how agentic commerce revenues might well be a bigger opportunity. 

Web firm type	Old primary economic engine	AI-era pressure	Likely new revenue
News publisher	Ads + subscriptions	AI answers substitute for clicks	AI licenses + subscriptions + events
Reference/data site	Ads	AI extracts information	Data/API licensing
UGC platform	Ads + engagement	AI absorbs user-generated knowledge	AI licensing + transactions
E-commerce	Product margin + ads	AI becomes shopping interface	Agent transactions + APIs + sponsored placement
Travel site	Ads + booking commissions	Agent bypasses comparison site	Agent booking commissions
Review site	Ads + affiliate	AI summarizes reviews	Licensing + affiliate/transaction fees
SaaS/web app	Subscription	Agent performs tasks without UI	API/agent usage fees
Search engine	Advertising	AI answer reduces external clicks	AI advertising + transactions
Social platform	Ads	AI consumes content without users	Licensing + commerce
Cloud/CDN/security provider	Infrastructure fees	Huge AI bot volume	Bot management + AI traffic infrastructure
Marketplace	Seller fees/ads	Agent becomes buyer interface	Transaction fees + agent APIs

And to the extent that advertising value shifts, it might shift in the direction of payments that optimize a supplier’s visibility in the candidate set or actual purchasing behavior. When an agent is searching hotels in a city with certain requirements, payment might take the form of paid placements to enhance inclusion, ranking, then selection and booking. 

Previously the scarce asset was supplying an audience. In the agentic AI era, value might shift to  proprietary information, trusted data, transaction capability and permission to act.

That might be an easier transition for commerce-oriented sellers, compared to content suppliers dependent on human visitors and advertising.